A novel approach to nose-tip and eye corners detection using H-K Curvature Analysis in case of 3D images
This addresses a domain-specific problem in 3D facial feature detection for computer vision applications, presenting an incremental improvement.
The paper tackles the problem of detecting nose-tip and eye corners in 3D face images under different poses using H-K curvature analysis, achieving 98.80% nose-tip detection and 72.20% eye corners detection on the FRAV3D Database.
In this paper we present a novel method that combines a HK curvature-based approach for three-dimensional (3D) face detection in different poses (X-axis, Y-axis and Z-axis). Salient face features, such as the eyes and nose, are detected through an analysis of the curvature of the entire facial surface. All the experiments have been performed on the FRAV3D Database. After applying the proposed algorithm to the 3D facial surface we have obtained considerably good results i.e. on 752 3D face images our method detected the eye corners for 543 face images, thus giving a 72.20% of eye corners detection and 743 face images for nose-tip detection thus giving a 98.80% of good nose tip localization